flair/upos-multi

Multilingual Universal Part-of-Speech Tagging in Flair (default model)

This is the default multilingual universal part-of-speech tagging model that ships with Flair.

F1-Score: 96.87 (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech)

Predicts universal POS tags:

tag	meaning
ADJ	adjective
ADP	adposition
ADV	adverb
AUX	auxiliary
CCONJ	coordinating conjunction
DET	determiner
INTJ	interjection
NOUN	noun
NUM	numeral
PART	particle
PRON	pronoun
PROPN	proper noun
PUNCT	punctuation
SCONJ	subordinating conjunction
SYM	symbol
VERB	verb
X	other

Based on Flair embeddings and LSTM-CRF.

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Demo: How to use in Flair

Requires: Flair (pip install flair)

from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("flair/upos-multi")

# make example sentence
sentence = Sentence("Ich liebe Berlin, as they say. ")

# predict POS tags
tagger.predict(sentence)

# print sentence
print(sentence)

# iterate over tokens and print the predicted POS label
print("The following POS tags are found:")
for token in sentence:
    print(token.get_label("upos"))

This yields the following output:

Token[0]: "Ich" → PRON (0.9999)
Token[1]: "liebe" → VERB (0.9999)
Token[2]: "Berlin" → PROPN (0.9997)
Token[3]: "," → PUNCT (1.0)
Token[4]: "as" → SCONJ (0.9991)
Token[5]: "they" → PRON (0.9998)
Token[6]: "say" → VERB (0.9998)
Token[7]: "." → PUNCT (1.0)

So, the words "Ich" and "they" are labeled as pronouns (PRON), while "liebe" and "say" are labeled as verbs (VERB) in the multilingual sentence "Ich liebe Berlin, as they say".

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Training: Script to train this model

The following Flair script was used to train this model:

from flair.data import MultiCorpus
from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \
    UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH
from flair.embeddings import StackedEmbeddings, FlairEmbeddings

# 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large)
corpus = MultiCorpus([
    UD_ENGLISH(in_memory=False),
    UD_GERMAN(in_memory=False),
    UD_DUTCH(in_memory=False),
    UD_FRENCH(in_memory=False),
    UD_ITALIAN(in_memory=False),
    UD_SPANISH(in_memory=False),
    UD_POLISH(in_memory=False),
    UD_CZECH(in_memory=False),
    UD_DANISH(in_memory=False),
    UD_SWEDISH(in_memory=False),
    UD_NORWEGIAN(in_memory=False),
    UD_FINNISH(in_memory=False),
])

# 2. what tag do we want to predict?
tag_type = 'upos'

# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)

# 4. initialize each embedding we use
embedding_types = [
    # contextual string embeddings, forward
    FlairEmbeddings('multi-forward'),

    # contextual string embeddings, backward
    FlairEmbeddings('multi-backward'),
]

# embedding stack consists of Flair embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)

# 5. initialize sequence tagger
from flair.models import SequenceTagger

tagger = SequenceTagger(hidden_size=256,
                        embeddings=embeddings,
                        tag_dictionary=tag_dictionary,
                        tag_type=tag_type,
                        use_crf=False)

# 6. initialize trainer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus)

# 7. run training
trainer.train('resources/taggers/upos-multi',
              train_with_dev=True,
              max_epochs=150)

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Cite

Please cite the following paper when using this model.

@inproceedings{akbik2018coling,
  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}
}

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Issues?

The Flair issue tracker is available here.